Optimal Service Placement, Request Routing and CPU Sizing in Cooperative Mobile Edge Computing Networks for Delay-Sensitive Applications

TL;DR

The paper tackles profit-driven joint optimization of service placement, request routing, and CPU sizing in cooperative mobile edge computing (MEC) networks for delay-sensitive applications. It proves the problem is NP-hard and develops a solution framework that combines a design parameter to balance delay requirements, a convex reformulation of delay constraints, and generalized Benders decomposition with primal decomposition and cutting planes. The approach decomposes the problem into a master integer program and a convex inner problem, with iterative cuts guaranteeing convergence to the global optimum. Numerical results show substantial improvements in SP profit, cache hit ratio, end-to-end delay, and cloud load compared with baselines, highlighting the practical value of a profit-oriented, near-optimal MEC resource management strategy.

Abstract

We study joint optimization of service placement, request routing, and CPU sizing in a cooperative MEC system. The problem is considered from the perspective of the service provider (SP), which delivers heterogeneous MEC-enabled delay-sensitive services, and needs to pay for the used resources to the mobile network operators and the cloud provider, while earning revenue from the served requests. We formulate the problem of maximizing the SP's total profit subject to the computation, storage, and communication constraints of each edge node and end-to-end delay requirements of the services as a mixed-integer non-convex optimization problem, and prove it to be NP-hard. To tackle the challenges in solving the problem, we first introduce a design trade-off parameter for different delay requirements of each service, which maintains flexibility in prioritizing them, and transform the original optimization problem by the new delay constraints. Then, by exploiting a hidden convexity, we reformulate the delay constraints into an equivalent form. Next, to handle the challenge of the complicating (integer) variables, using primal decomposition, we decompose the problem into an equivalent form of master and inner sub-problems over the mixed and real variables, respectively. We then employ a cutting-plane approach for building up adequate representations of the extremal value of the inner problem as a function of the complicating variables and the set of values of the complicating variables for which the inner problem is feasible. Finally, we propose a solution strategy based on generalized Benders decomposition and prove its convergence to the optimal solution within a limited number of iterations. Extensive simulation results demonstrate that the proposed scheme significantly outperforms the existing mechanisms in terms of the SP's profit, cache hit ratio, running time, and end-to-end delay.

Figures (5)

• Figure 1: An example MEC system leased and owned by the MNO with service placement and request routing.
• Figure 2: Comparison of the profit of the SP in the proposed scheme and the baselines.
• Figure 3: Comparison of the running time of the proposed algorithm and the baselines.
• Figure 4: Comparison of the cache hit ratio in the proposed scheme and the baselines.
• Figure 5: Comparison of the average end-to-end delay in the proposed scheme and the baselines.

Theorems & Definitions (13)

• Theorem 4.1: Problem Complexity
• proof
• Remark 1: Complexity of the Inner and Outer Problems
• Lemma 6.1
• proof
• Lemma 6.2
• proof
• Lemma 6.3
• proof
• Lemma 6.4